Road detection via entropy By Anna Zaidman 1 1 What is entropy? - - PowerPoint PPT Presentation

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By Anna Zaidman

Road detection via entropy

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What is entropy?

Entropy is a mathematically - defined thermodynamic quantity that helps to account for the flow of energy through a thermodynamic process.

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What is entropy?

As a mathematical function, entropy is used to measure the level of “disorder” within a certain sample of values, a certain entropy value is assigned to a pixel by measuring entropy of a sample of pixel values present in a given “window” around that pixel. Smooth and visually “uniform” parts of the image thus have low or very low entropy (no matter what are the real pixel values - “color”), while areas with higher diversity of pixel values, especially with sudden shifts in image color – gain higher entropy values.

What is entropy?

If we assume that the road to follow has a consistent color or texture, we can state that the neighborhoods of pixels forming that road have lower diversification than a neighborhood of pixels forming people, trees, cars, traffic signs, and so on, which might be composed of different levels

• f illumination, textures, patterns and edges.

Lack of diversity means also, lack of information, when the opposite implies lot of information.

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My goal is to detect a drivable road using an adaptive entropy filter .

How to create an entropy filter?

Let's define the entropy H of an image I, as the amount of information that exists over a statistically distributed set of pixels 𝑕, over a window w, where for each grayscale level i its probability of appearance is 𝒒(𝒉𝒋) , as shown in equation (1). The probability of a graylevel i is calculated according to the histogram of the image as shown in equation (2). (1) 𝐼(𝑕) = − 𝑞 𝑕𝑗 𝑚𝑝𝑕 (𝑞 𝑕𝑗 )

𝑥

(2 ) 𝑞 𝑕𝑗 =

ℎ𝑗𝑡𝑢(𝑕𝑗) ℎ𝑗𝑡𝑢(𝑕𝑘)

𝑓𝑜𝑒 𝑘=1

Thus , an entropy filter consists of calculating the entropy over all the pixels in the image, using a predefined mask of neighborhood.

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Implementation

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Conclusions

The entropy filter turned out to be a robust technique to perform segmentation and Clustering. A setback is the implementation of this method in real time. Due to the nature of the entropy filter , the computational load is very expensive.

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How can we resolve it?

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In order to improve performance , look-up-tables with a priori data could be used to compute the entropy itself .

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Use other filters with similar results but faster computing.